



"""simple docstring"""





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCamelCase  			=		False
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       pass



@nightly
@require_torch_gpu
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    Dict       )					->		Tuple:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       def 	lowercase__							(				self     :    Optional[Any]       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"       )
             # remove text_unet
             pipe.remove_unused_weights()
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger "
             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							guidance_scale=7.5						,							num_inference_steps=2						,							output_type="numpy"       ).images

             with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(_UpperCAmelCase       )
                   UpperCAmelCase_     					=				VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				generator.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							guidance_scale=7.5						,							num_inference_steps=2						,							output_type="numpy"       ).images

             assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"







       def 	lowercase__							(				self     :    Dict       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				VersatileDiffusionTextToImagePipeline.from_pretrained(
                 "shi-labs/versatile-diffusion"						,							torch_dtype=torch.floataa       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger "
             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							generator=_UpperCAmelCase						,							guidance_scale=7.5						,							num_inference_steps=50						,							output_type="numpy"       ).images

             UpperCAmelCase_     					=				image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_     					=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2












"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			len(a__ )
						print('The following activities are selected:' )

						# The first activity is always selected
						lowerCAmelCase   :Dict						=			0
						print(a__     ,      end=',' )

						# Consider rest of the activities
						for j in range(a__ ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(a__     ,      end=',' )
																		lowerCAmelCase   :List[str]						=			j


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__SCREAMING_SNAKE_CASE   =   [1, 3, 0, 5, 8, 5]
		__SCREAMING_SNAKE_CASE   =   [2, 4, 6, 7, 9, 9]
		print_max_activities(start, finish)